





'''simple docstring'''






import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
					import tensorflow as tf
					from tensorflow.python.eager import context
					from tensorflow.python.framework import ops

					from transformers import GradientAccumulator, create_optimizer
@require_tf
class 		lowerCamelCase	(   unittest.TestCase							):



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_						):
			self.assertEqual(len(a_						)		,   len(a_						)						)
			for a, b in zip(a_		,   a_						):
					self.assertAlmostEqual(a_		,   a_		,   delta=a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Any       						= GradientAccumulator()
			accumulator([tf.constant([1.0, 2.0]						)]						)
			accumulator([tf.constant([-2.0, 1.0]						)]						)
			accumulator([tf.constant([-1.0, 2.0]						)]						)
			with self.assertRaises(a_						):
					accumulator([tf.constant([1.0, 1.0]						), tf.constant([2.0, 2.0]						)]						)
			self.assertEqual(accumulator.step		,   3						)
			self.assertEqual(len(accumulator.gradients						)		,   1						)
			self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,   [-2.0, 5.0]		,   tol=1e-2						)
			accumulator.reset()
			self.assertEqual(accumulator.step		,   0						)
			self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,   [0.0, 0.0]		,   tol=1e-2						)





	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     List[str]       						= None
			ops.enable_eager_execution_internal()
			lowerCAmelCase  :     Any       						= tf.config.list_physical_devices("CPU"						)
			if len(a_						) == 1:
					tf.config.set_logical_device_configuration(
					    physical_devices[0]		,   [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]						)
			lowerCAmelCase  :     Optional[int]       						= tf.config.list_logical_devices(device_type="CPU"						)
			lowerCAmelCase  :     Optional[Any]       						= tf.distribute.MirroredStrategy(devices=devices[:2]						)

			with strategy.scope():
					lowerCAmelCase  :     List[str]       						= GradientAccumulator()
					lowerCAmelCase  :     List[str]       						= tf.Variable([4.0, 3.0]						)
					lowerCAmelCase						,		lowerCAmelCase  :     Dict       						= create_optimizer(5e-5		,   10		,   5						)
					lowerCAmelCase  :     Optional[int]       						= tf.Variable([0.0, 0.0]		,   trainable=a_						)

			def accumulate_on_replica(a_						):
					accumulator([gradient]						)

			def apply_on_replica():
					optimizer.apply_gradients(list(zip(accumulator.gradients		,   [variable]						)						)						)

			@tf.function
			def accumulate(a_		,   a_						):
					with strategy.scope():
							lowerCAmelCase  :     int       						= strategy.experimental_local_results(a_						)
							local_variables[0].assign(a_						)
							local_variables[1].assign(a_						)
							strategy.run(a_		,   args=(gradient_placeholder,)						)

			@tf.function
			def apply_grad():
					with strategy.scope():
							strategy.run(a_						)

			def _check_local_values(a_		,   a_						):
					lowerCAmelCase  :     Any       						= strategy.experimental_local_results(accumulator._gradients[0]						)
					self.assertListAlmostEqual(values[0].value()		,   a_		,   tol=1e-2						)
					self.assertListAlmostEqual(values[1].value()		,   a_		,   tol=1e-2						)

			accumulate([1.0, 2.0]		,   [-1.0, 1.0]						)
			accumulate([3.0, -1.0]		,   [-1.0, -1.0]						)
			accumulate([-2.0, 2.0]		,   [3.0, -2.0]						)
			self.assertEqual(accumulator.step		,   3						)
			_check_local_values([2.0, 3.0]		,   [1.0, -2.0]						)
			apply_grad()
			self.assertListAlmostEqual(variable.value()		,   [4.0, 3.0]		,   tol=1e-2						)
			accumulator.reset()
			self.assertEqual(accumulator.step		,   0						)
			_check_local_values([0.0, 0.0]		,   [0.0, 0.0]						)










'''simple docstring'''






import random
from typing import Any






def   __A    (				a_    :	list						):
		for _ in range(len(a_						)						):
				lowerCAmelCase  :     List[Any]       						= random.randint(0   ,len(a_						) - 1						)
				lowerCAmelCase  :     Tuple       						= random.randint(0   ,len(a_						) - 1						)
				lowerCAmelCase						,		lowerCAmelCase  :     Optional[Any]       						= data[b], data[a]
		return data


if __name__ == "__main__":
					lowerCAmelCase									=   [0, 1, 2, 3, 4, 5, 6, 7]
					lowerCAmelCase									=   ["""python""", """says""", """hello""", """!"""]
					print("""Fisher-Yates Shuffle:""")
					print("""List""", integers, strings)
					print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))



